Remove and rename tf_optimizer

ml-agents/mlagents/trainers/common/nn_policy.py

         :param resample: Whether we are using the resampling trick to update the policy in continuous output.
         """
         super().__init__(seed, brain, trainer_params, load)
-        self.tf_optimizer: Optional[tf.train.Optimizer] = None
         self.grads = None
         self.update_batch: Optional[tf.Operation] = None
         num_layers = trainer_params["num_layers"]

ml-agents/mlagents/trainers/common/tf_optimizer.py

             )
             self.update_dict.update(self.reward_signals[reward_signal].update_dict)
 
-    def create_tf_optimizer(
+    def create_optimizer_op(
         self, learning_rate: float, name: str = "Adam"
     ) -> tf.train.Optimizer:
         return tf.train.AdamOptimizer(learning_rate=learning_rate, name=name)

ml-agents/mlagents/trainers/sac/optimizer.py

         Creates the Adam optimizers and update ops for SAC, including
         the policy, value, and entropy updates, as well as the target network update.
         """
-        policy_optimizer = self.create_tf_optimizer(
+        policy_optimizer = self.create_optimizer_op(
-        entropy_optimizer = self.create_tf_optimizer(
+        entropy_optimizer = self.create_optimizer_op(
-        value_optimizer = self.create_tf_optimizer(
+        value_optimizer = self.create_optimizer_op(
             learning_rate=self.learning_rate, name="sac_value_opt"
         )
 

ml-agents/mlagents/trainers/tf_policy.py

         )
         tf.set_random_seed(seed)
         self.saver = None
-        self.tf_optimizer = None
         if self.use_recurrent:
             self.m_size = trainer_parameters["memory_size"]
             self.sequence_length = trainer_parameters["sequence_length"]